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Evaluation and statistical inference for human connectomes

2014; Nature Portfolio; Volume: 11; Issue: 10 Linguagem: Inglês

10.1038/nmeth.3098

1548-7105

Franco Pestilli, Jason D. Yeatman, Ariel Rokem, Kendrick Kay, Brian A. Wandell,

Functional Brain Connectivity Studies

LiFE is an algorithm that evaluates human connectome models derived from magnetic resonance imaging (MRI) and tractography methods. The algorithm achieves this goal by assessing the contribution of all the fiber tracts in a connectome to predict the measured MRI signal. Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.